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    "# Practical 8: Feature Selection and Dimensionality Reduction  \n",
    "\n",
    "\n",
    "Upon completion of this session you should be able to:\n",
    "- understand how feature selection and dimensionality reduction methods such as PCA work.\n",
    "- be able to apply these methods in Python to select feature and/or reduce dimensionality.\n",
    "\n",
    "---\n",
    "- Materials in this module include resources collected from various open-source online repositories.\n",
    "- Jupyter source file can be downloaded from https://github.com/gaoshangdeakin/SIT384-Jupyter\n",
    "- If you found any issue/bug for this document, please submit an issue at [https://github.com/gaoshangdeakin/SIT384/issues](https://github.com/gaoshangdeakin/SIT384/issues)\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "\n",
    "This practical session will demonstrate feature selection and dimensionality reduction methods, such as PCA.\n",
    "\n",
    "\n",
    "## Background\n",
    "\n",
    "\n",
    "### Part 1 Feature Selection\n",
    "\n",
    "1.1 [Baseline models](#1)\n",
    "\n",
    "1.2 [Remove features with missing values](#2)\n",
    "\n",
    "1.3 [Remove features with low variance](#3)\n",
    "\n",
    "1.4 [Remove highly correlated features](#4)\n",
    "\n",
    "1.5 [Univariate Feature Selection](#5)\n",
    "\n",
    "1.6 [Recursive Feature Elimination](#6)\n",
    "\n",
    "1.7 [Feature selection using SelectFromModel](#7)\n",
    "\n",
    "1.8 [PCA](#8)\n",
    "\n",
    "### Part 2 PCA and Dimensionality Reduction \n",
    "\n",
    "2.1 [Principal Component Analysis](#pca)\n",
    "\n",
    "2.2 [Other Demensionality Reducting Routines](#others)\n",
    "\n",
    "\n",
    "## Tasks\n",
    "\n",
    "## Summary\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## <span style=\"color:#0b486b\">1. Feature Selection</span>\n",
    "\n",
    "\n",
    "According to [wikipedia](https://en.wikipedia.org/wiki/Feature_selection), \"feature selection is the process of selecting a subset of relevent features for use in model construction\".  In normal circumstances, domain knowledge plays an important role.  However, sometimes we are given a binary target and 300 continuous variables \"of mysterious origin\" which forces us to try automatic feature selection techniques.\n",
    "\n",
    "In this session we will explore the following feature selection and dimensionality reduction techniques:\n",
    "\n",
    "1. Remove features with missing values\n",
    "2. Remove features with low variance\n",
    "3. Remove highly correlated features\n",
    "4. Univariate feature selection\n",
    "5. Recursive feature elimination\n",
    "6. Feature selection using SelectFromModel\n",
    "7. PCA\n",
    "\n",
    "\n",
    "\n",
    "### <span style=\"color:#0b486b\">Feature Selection vs Dimensionality Reduction</span>\n",
    "\n",
    "Often, feature selection and dimensionality reduction are grouped together (like here in this session). While both methods are used for reducing the number of features in a dataset, there is an important difference.\n",
    "\n",
    "Feature selection is simply selecting and excluding given features without changing them.\n",
    "\n",
    "Dimensionality reduction transforms features into a lower dimension.\n",
    "\n",
    "In this session we will explore the following feature selection and dimensionality reduction techniques:\n",
    "Feature Selection\n",
    "\n",
    "    Remove features with missing values\n",
    "    Remove features with low variance\n",
    "    Remove highly correlated features\n",
    "    Univariate feature selection\n",
    "    Recursive feature elimination\n",
    "    Feature selection using SelectFromModel\n",
    "\n",
    "Dimensionality Reduction\n",
    "\n",
    "    PCA\n",
    "\n",
    "---\n",
    "<a id = \"1\"></a>\n",
    "\n",
    "### <span style=\"color:#0b486b\">1.1 Baseline Model</span>\n",
    "\n",
    "We’ll use logistic regression as our baseline model. Dataset can be retrieved from [here](https://github.com/gaoshangdeakin/SIT384/raw/master/Feature_selection_dataset.zip) or downloaded from the corresponding week folder on clouddeakin. \n",
    "\n",
    "For the given dataset, you are predicting the binary target associated with each row, without overfitting to the minimal set of training examples provided.\n",
    "\n",
    "Files\n",
    "\n",
    "    train.csv - the training set. 250 rows.\n",
    "    test.csv - the test set. 19,750 rows.\n",
    "    sample_submission.csv - a sample submission file in the correct format\n",
    "\n",
    "Columns\n",
    "\n",
    "    id- sample id\n",
    "    target- a binary target of mysterious origin.\n",
    "    0-299- continuous variables.\n",
    "\n",
    "We first split into test and train sets and scale the data: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# setting up default plotting parameters\n",
    "%matplotlib inline\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [20.0, 7.0]\n",
    "plt.rcParams.update({'font.size': 22,})\n",
    "\n",
    "sns.set_palette('viridis')\n",
    "sns.set_style('white')\n",
    "sns.set_context('talk', font_scale=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Shape:  (250, 302)\n",
      "Test Shape:  (19750, 301)\n"
     ]
    },
    {
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       "      <td>-1.695</td>\n",
       "      <td>-1.257</td>\n",
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       "      <td>-0.808</td>\n",
       "      <td>-1.624</td>\n",
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       "      <td>-0.023</td>\n",
       "      <td>-0.172</td>\n",
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       "      <td>0.013</td>\n",
       "      <td>0.263</td>\n",
       "      <td>-1.222</td>\n",
       "      <td>0.726</td>\n",
       "      <td>1.444</td>\n",
       "      <td>-1.165</td>\n",
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       "      <td>0.004</td>\n",
       "      <td>0.800</td>\n",
       "      <td>-1.211</td>\n",
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       "   id  target      0      1      2      3      4      5      6      7  ...  \\\n",
       "0   0     1.0 -0.098  2.165  0.681 -0.614  1.309 -0.455 -0.236  0.276  ...   \n",
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       "3   3     1.0  0.067 -0.021  0.392 -1.637 -0.446 -0.725 -1.035  0.834  ...   \n",
       "4   4     1.0  2.347 -0.831  0.511 -0.021  1.225  1.594  0.585  1.509  ...   \n",
       "\n",
       "     290    291    292    293    294    295    296    297    298    299  \n",
       "0  0.867  1.347  0.504 -0.649  0.672 -2.097  1.051 -0.414  1.038 -1.065  \n",
       "1 -0.165 -1.695 -1.257  1.359 -0.808 -1.624 -0.458 -1.099 -0.936  0.973  \n",
       "2  0.013  0.263 -1.222  0.726  1.444 -1.165 -1.544  0.004  0.800 -1.211  \n",
       "3 -0.404  0.640 -0.595 -0.966  0.900  0.467 -0.562 -0.254 -0.533  0.238  \n",
       "4  0.898  0.134  2.415 -0.996 -1.006  1.378  1.246  1.478  0.428  0.253  \n",
       "\n",
       "[5 rows x 302 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv('./data/p08/train.csv')\n",
    "test = pd.read_csv('./data/p08/test.csv')\n",
    "\n",
    "print('Train Shape: ', train.shape)\n",
    "print('Test Shape: ', test.shape)\n",
    "\n",
    "train.head()\n",
    "#print()\n",
    "#test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# using seaborns countplot to show distribution of questions in dataset\n",
    "fig, ax = plt.subplots()\n",
    "g = sns.countplot(train.target, palette='viridis')\n",
    "g.set_xticklabels(['0', '1'])\n",
    "g.set_yticklabels([])\n",
    "\n",
    "# function to show values on bars\n",
    "def show_values_on_bars(axs):\n",
    "    def _show_on_single_plot(ax):        \n",
    "        for p in ax.patches:\n",
    "            _x = p.get_x() + p.get_width() / 2\n",
    "            _y = p.get_y() + p.get_height()\n",
    "            value = '{:.0f}'.format(p.get_height())\n",
    "            ax.text(_x, _y, value, ha=\"center\") \n",
    "\n",
    "    if isinstance(axs, np.ndarray):\n",
    "        for idx, ax in np.ndenumerate(axs):\n",
    "            _show_on_single_plot(ax)\n",
    "    else:\n",
    "        _show_on_single_plot(axs)\n",
    "show_values_on_bars(ax)\n",
    "\n",
    "sns.despine(left=True, bottom=True)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "plt.title('Distribution of Target', fontsize=30)\n",
    "plt.tick_params(axis='x', which='major', labelsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Baseline Models\n",
    "We'll use logistic regression as it is fast to train and predict and scales well.  We'll also use random forest.  With  its attribute feature_importances, we can get a sense of which features are most important.\n",
    "\n",
    "<!--The logistic regression baseline model scored 0.666 on the public leaderboard.-->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prepare for modeling\n",
    "X_train_df = train.drop(['id', 'target'], axis=1)\n",
    "y_train = train['target']\n",
    "\n",
    "X_test = test.drop(['id'], axis=1)\n",
    "\n",
    "# scaling data\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train_df)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Scores:  [0.80729167 0.71875    0.734375   0.80034722 0.66319444]\n",
      "RFC Scores:  [0.59375    0.68402778 0.67100694 0.66579861 0.58246528]\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression(solver='liblinear')\n",
    "rfc = RandomForestClassifier(n_estimators=100)\n",
    "\n",
    "lr_scores = cross_val_score(lr,\n",
    "                            X_train,\n",
    "                            y_train,\n",
    "                            cv=5,\n",
    "                            scoring='roc_auc')\n",
    "rfc_scores = cross_val_score(rfc, X_train, y_train, cv=5, scoring='roc_auc')\n",
    "\n",
    "print('LR Scores: ', lr_scores)\n",
    "print('RFC Scores: ', rfc_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[248  18 218   8  23  79 131   1  20 270  11 204  98 267 215 286 245 109\n",
      "  44  77 142 291 264  49  59 172  85 185  99 136  10 252 102  75 186  40\n",
      " 100 236 104 159 265  55  29 259 229 196 122 206 211 155 145 187  89 209\n",
      " 284  86 148  22  34 235 182 294  54 160 168 161  84 277  93 283 271 202\n",
      "  87   3  53 210  69 290  17 263  61  95 269 132 262 112  41  46 128 282\n",
      "  37 152 149 220 118  42  63 197 244  12 153 293 275  57  50 223 297 213\n",
      " 157 288 191 207 212  25  28 105 260 296 125 178  88 192 228  32  21 281\n",
      "  67 166 146 254 200  76 274 179 256  96 154 174  36 173 119 151 181  35\n",
      " 273 162  83 280 249 135 150 103  27 139  92 190 216  70 255 257 287  31\n",
      " 158   6 226 130 299  60 120 238  94 205 143 134  43 141 222 298  58  47\n",
      "  71 115 184 242  81  19  97 243  52 240  80 224 140 278 289  66   2 253\n",
      " 261 107 227 137 114  38 241   5 170 285 101 167 247 169 233 266 110  62\n",
      " 214 163 276 232 279 221 208 198  72 129 138 193 292 123 203  73 239 124\n",
      "  45  15 106 195  68 225  26 250 111 180 188  13 199 165 133  30   0  64\n",
      " 219 108   4  51  74 164  16 230 171 246 258  82  48 144 113 177 175  14\n",
      " 127 147  78 231   9   7 183 116 201 272  90 268 121 126 156  56 251 234\n",
      "  24 189 295 176 194  91  39 237  65 117 217  33]\n",
      "\n",
      "[ 90 268 121 126 156  56 251 234  24 189 295 176 194  91  39 237  65 117\n",
      " 217]\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking which are the most important features\n",
    "feature_importance = rfc.fit(X_train, y_train).feature_importances_\n",
    "\n",
    "# Make importances relative to max importance.\n",
    "feature_importance = 100.0 * (feature_importance / feature_importance.max())\n",
    "\n",
    "#numpy.argsort() function is used to perform an indirect sort \n",
    "#along the given axis using the algorithm specified by the kind keyword. \n",
    "#It returns an array of indices of the same shape as arr that would sort the array.\n",
    "#https://www.geeksforgeeks.org/numpy-argsort-in-python/ \n",
    "sorted_idx = np.argsort(feature_importance)\n",
    "print(sorted_idx)\n",
    "print()\n",
    "#the slicing syntax has supported an optional third ``step'' or ``stride'' argument\n",
    "#get the indices from the 20th index from the end to the second last. The last is not included. \n",
    "#the last is feature_importance.max() and it's relative importance is 100%\n",
    "#if you use: sorted_idx = sorted_idx[-20::1], you will see this 100% relative importance at the top of the plot\n",
    "sorted_idx = sorted_idx[-20:-1:1]\n",
    "print(sorted_idx)\n",
    "pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "plt.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "plt.yticks(pos, X_train_df.columns[sorted_idx])\n",
    "plt.xlabel('Relative Importance')\n",
    "plt.title('Feature Importance', fontsize=30)\n",
    "plt.tick_params(axis='x', which='major', labelsize=15)\n",
    "sns.despine(left=True, bottom=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While logistic regression is clearly the superior model, we can see that both modesl are likely overfitting from the fluctuating [cross validation scores](https://machinelearningmastery.com/k-fold-cross-validation/). We can apply feature selection techniques to improve model performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id = \"2\"></a>\n",
    "\n",
    "### <span style=\"color:#0b486b\">1.2 Remove features with missing values</span>\n",
    "\n",
    "This one is pretty self explanatory.  First we check for missing values and then can remove columns exceeding a threshold we define."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check missing values\n",
    "train.isnull().any().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset has no missing values and therefore no features to remove at this step."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id = \"3\"></a>\n",
    "\n",
    "### <span style=\"color:#0b486b\">1.3 Remove features with low variance</span>\n",
    "\n",
    "In sklearn's feature selection module we find VarianceThreshold.  It removes all features whose variance doesn't meet some threshold.  By default it removes features with zero variance or features that have the same value for all samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(250, 302)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import feature_selection\n",
    "\n",
    "sel = feature_selection.VarianceThreshold()\n",
    "train_variance = sel.fit_transform(train)\n",
    "train_variance.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The competition description stated that our features are all continuous.  We can see from above there are no features with the same value in all columns, so we have no features to remove here.  We can revisit this technique later and consider removing features with low variance later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id = \"4\"></a>\n",
    "\n",
    "### <span style=\"color:#0b486b\">1.4 Remove highly correlated features</span>\n",
    "\n",
    "Features that are highly correlated or colinear can cause overfitting.  Here we will explore correlations among features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "target    1.000000\n",
      "33        0.373608\n",
      "65        0.293846\n",
      "217       0.207215\n",
      "117       0.197496\n",
      "91        0.192536\n",
      "24        0.173096\n",
      "295       0.170501\n",
      "73        0.167557\n",
      "183       0.164146\n",
      "Name: target, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# find correlations to target\n",
    "corr_matrix = train.corr().abs()\n",
    "\n",
    "print(corr_matrix['target'].sort_values(ascending=False).head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select upper triangle of correlation matrix\n",
    "matrix = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))\n",
    "sns.heatmap(matrix)\n",
    "plt.show;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns to drop:  0\n"
     ]
    }
   ],
   "source": [
    "# Find index of feature columns with high correlation\n",
    "to_drop = [column for column in matrix.columns if any(matrix[column] > 0.50)]\n",
    "print('Columns to drop: ' , (len(to_drop)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above correlation matrix we see that there are no highly correlated features in the dataset.  And even exploring correlation to target shows feature 33 with the highest correlation of only 0.37."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id = \"5\"></a>\n",
    "\n",
    "### <span style=\"color:#0b486b\">1.5 Univariate Feature Selection</span>\n",
    "\n",
    "We can use sklearn's SelectKBest to select a number of features to keep.  This method uses statistical tests to select features having the highest correlation to the target.  Here we will keep the top 100 features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# feature extraction\n",
    "k_best = feature_selection.SelectKBest(score_func=feature_selection.f_classif, k=100)\n",
    "# fit on train set\n",
    "fit = k_best.fit(X_train, y_train)\n",
    "# transform train set\n",
    "univariate_features = fit.transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Scores:  [0.89930556 0.93402778 0.89236111 0.96006944 0.94791667]\n",
      "RFC Scores:  [0.7890625  0.86458333 0.81770833 0.87760417 0.75173611]\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression(solver='liblinear')\n",
    "rfc = RandomForestClassifier(n_estimators=100)\n",
    "\n",
    "lr_scores = cross_val_score(lr, univariate_features, y_train, cv=5, scoring='roc_auc')\n",
    "rfc_scores = cross_val_score(rfc, univariate_features, y_train, cv=5, scoring='roc_auc')\n",
    "\n",
    "print('LR Scores: ', lr_scores)\n",
    "print('RFC Scores: ', rfc_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking which are the most important features\n",
    "feature_importance = rfc.fit(univariate_features, y_train).feature_importances_\n",
    "\n",
    "# Make importances relative to max importance.\n",
    "feature_importance = 100.0 * (feature_importance / feature_importance.max())\n",
    "sorted_idx = np.argsort(feature_importance)\n",
    "sorted_idx = sorted_idx[-20:-1:1]\n",
    "pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "plt.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "plt.yticks(pos, X_train_df.columns[sorted_idx])\n",
    "plt.xlabel('Relative Importance')\n",
    "plt.title('Feature Importance', fontsize=30)\n",
    "plt.tick_params(axis='x', which='major', labelsize=15)\n",
    "sns.despine(left=True, bottom=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our cross validation scores are improved as compared to the baseline above, but we still can see variation in the scores which indicates overfitting.  \n",
    "\n",
    "<!--Logistic Regression Public Leaderboard Score: 0.676-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id = \"6\"></a>\n",
    "\n",
    "### <span style=\"color:#0b486b\">1.6 Recursive Feature Elimination</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recursive feature selection works by eliminating the least important features. It continues recursively until the specified number of features is reached. Recursive elimination can be used with any model that assigns weights to features, either through coef_ or feature\\_importances_ . \n",
    "  \n",
    "Here we will use Random Forest to select the 100 best features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# feature extraction\n",
    "rfe = feature_selection.RFE(lr, n_features_to_select=100)\n",
    "\n",
    "# fit on train set\n",
    "fit = rfe.fit(X_train, y_train)\n",
    "\n",
    "# transform train set\n",
    "recursive_features = fit.transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Scores:  [0.99826389 0.99652778 0.984375   1.         0.99652778]\n",
      "RFC Scores:  [0.63020833 0.67708333 0.54166667 0.81510417 0.54166667]\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression(solver='liblinear')\n",
    "rfc = RandomForestClassifier(n_estimators=10)\n",
    "\n",
    "lr_scores = cross_val_score(lr, recursive_features, y_train, cv=5, scoring='roc_auc')\n",
    "rfc_scores = cross_val_score(rfc, recursive_features, y_train, cv=5, scoring='roc_auc')\n",
    "\n",
    "print('LR Scores: ', lr_scores)\n",
    "print('RFC Scores: ', rfc_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AChEKAQAAAAAAWKH/BWo+C2anAa6yAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking which are the most important features\n",
    "feature_importance = rfc.fit(recursive_features, y_train).feature_importances_\n",
    "\n",
    "# Make importances relative to max importance.\n",
    "feature_importance = 100.0 * (feature_importance / feature_importance.max())\n",
    "sorted_idx = np.argsort(feature_importance)\n",
    "sorted_idx = sorted_idx[-20:-1:1]\n",
    "pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "plt.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "plt.yticks(pos, X_train_df.columns[sorted_idx])\n",
    "plt.xlabel('Relative Importance')\n",
    "plt.title('Feature Importance', fontsize=30)\n",
    "plt.tick_params(axis='x', which='major', labelsize=15)\n",
    "sns.despine(left=True, bottom=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--Logistic Regression Public Leaderboard Score: 0.667 -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id = \"7\"></a>\n",
    "\n",
    "### <span style=\"color:#0b486b\">1.7 Feature selection using SelectFromModel</span>\n",
    "\n",
    "Like recursive feature selection, sklearn's SelectFromModel is used with any estimator that has a coef_ or feature\\_importances_ attribute. It removes features with values below a set threshold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# feature extraction\n",
    "select_model = feature_selection.SelectFromModel(lr)\n",
    "\n",
    "# fit on train set\n",
    "fit = select_model.fit(X_train, y_train)\n",
    "\n",
    "# transform train set\n",
    "model_features = fit.transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Scores:  [0.984375   0.99479167 0.97222222 0.99305556 0.99305556]\n",
      "RFC Scores:  [0.83940972 0.81163194 0.79600694 0.83072917 0.79427083]\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression(solver='liblinear')\n",
    "rfc = RandomForestClassifier(n_estimators=100)\n",
    "\n",
    "lr_scores = cross_val_score(lr, model_features, y_train, cv=5, scoring='roc_auc')\n",
    "rfc_scores = cross_val_score(rfc, model_features, y_train, cv=5, scoring='roc_auc')\n",
    "\n",
    "print('LR Scores: ', lr_scores)\n",
    "print('RFC Scores: ', rfc_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking which are the most important features\n",
    "feature_importance = rfc.fit(model_features, y_train).feature_importances_\n",
    "\n",
    "# Make importances relative to max importance.\n",
    "feature_importance = 100.0 * (feature_importance / feature_importance.max())\n",
    "sorted_idx = np.argsort(feature_importance)\n",
    "sorted_idx = sorted_idx[-20:-1:1]\n",
    "pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "plt.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "plt.yticks(pos, X_train_df.columns[sorted_idx])\n",
    "plt.xlabel('Relative Importance')\n",
    "plt.title('Feature Importance', fontsize=30)\n",
    "plt.tick_params(axis='x', which='major', labelsize=15)\n",
    "sns.despine(left=True, bottom=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id = \"8\"></a>\n",
    "\n",
    "### <span style=\"color:#0b486b\">1.8 PCA</span>\n",
    "\n",
    "PCA (Principle Component Analysis) is a dimensionality reduction technique that projects the data into a lower dimensional space. While there are many effective dimensionality reduction techniques, PCA is the only example we will explore here. PCA can be useful in many situations, but especially in cases with excessive multicollinearity or explanation of predictors is not a priority. For detailes of PCA, please see below section 2 of the lab.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(250, 139)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "# pca - keep 90% of variance\n",
    "pca = PCA(0.90)\n",
    "\n",
    "principal_components = pca.fit_transform(X_train)\n",
    "principal_df = pd.DataFrame(data = principal_components)\n",
    "principal_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Scores:  [0.80902778 0.703125   0.734375   0.80555556 0.66145833]\n",
      "RFC Scores:  [0.5390625  0.68923611 0.62239583 0.6171875  0.51736111]\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression(solver='liblinear')\n",
    "rfc = RandomForestClassifier(n_estimators=100)\n",
    "\n",
    "lr_scores = cross_val_score(lr, principal_df, y_train, cv=5, scoring='roc_auc')\n",
    "rfc_scores = cross_val_score(rfc, principal_df, y_train, cv=5, scoring='roc_auc')\n",
    "\n",
    "print('LR Scores: ', lr_scores)\n",
    "print('RFC Scores: ', rfc_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(250, 93)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pca keep 75% of variance\n",
    "pca = PCA(0.75)\n",
    "principal_components = pca.fit_transform(X_train)\n",
    "principal_df = pd.DataFrame(data = principal_components)\n",
    "principal_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Scores:  [0.72048611 0.60069444 0.68402778 0.71006944 0.61284722]\n",
      "RFC Scores:  [0.63541667 0.5        0.55902778 0.65625    0.70833333]\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression(solver='liblinear')\n",
    "rfc = RandomForestClassifier(n_estimators=100)\n",
    "\n",
    "lr_scores = cross_val_score(lr, principal_df, y_train, cv=5, scoring='roc_auc')\n",
    "rfc_scores = cross_val_score(rfc, principal_df, y_train, cv=5, scoring='roc_auc')\n",
    "\n",
    "print('LR Scores: ', lr_scores)\n",
    "print('RFC Scores: ', rfc_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking which are the most important features\n",
    "feature_importance = rfc.fit(principal_df, y_train).feature_importances_\n",
    "\n",
    "# Make importances relative to max importance.\n",
    "feature_importance = 100.0 * (feature_importance / feature_importance.max())\n",
    "sorted_idx = np.argsort(feature_importance)\n",
    "sorted_idx = sorted_idx[-20:-1:1]\n",
    "pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "plt.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "plt.yticks(pos, X_train_df.columns[sorted_idx])\n",
    "plt.xlabel('Relative Importance')\n",
    "plt.title('Feature Importance', fontsize=30)\n",
    "plt.tick_params(axis='x', which='major', labelsize=15)\n",
    "sns.despine(left=True, bottom=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recursive feature selection appears to perform the best above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9059027777777778"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# feature extraction\n",
    "rfe = feature_selection.RFE(lr, n_features_to_select=100)\n",
    "\n",
    "# fit on train set\n",
    "fit = rfe.fit(X_train, y_train)\n",
    "\n",
    "# transform train set\n",
    "recursive_X_train = fit.transform(X_train)\n",
    "recursive_X_test = fit.transform(X_test)\n",
    "\n",
    "lr = LogisticRegression(C=1, class_weight={1:0.6, 0:0.4}, penalty='l1', solver='liblinear')\n",
    "lr_scores = cross_val_score(lr, recursive_X_train, y_train, cv=5, scoring='roc_auc')\n",
    "lr_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = lr.fit(recursive_X_train, y_train).predict_proba(recursive_X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>250</td>\n",
       "      <td>0.270270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>251</td>\n",
       "      <td>0.401559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>252</td>\n",
       "      <td>0.144903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>253</td>\n",
       "      <td>0.003880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>254</td>\n",
       "      <td>0.177721</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    id    target\n",
       "0  250  0.270270\n",
       "1  251  0.401559\n",
       "2  252  0.144903\n",
       "3  253  0.003880\n",
       "4  254  0.177721"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission = pd.read_csv('./data/p08/sample_submission.csv')\n",
    "submission['target'] = predictions\n",
    "submission.to_csv('submission.csv', index=False)\n",
    "submission.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## <span style=\"color:#0b486b\">2. Dimensionality Reduction and PCA</span>\n",
    "\n",
    "We will be looking at Principal Component Analysis <b>PCA</b>, which is an extremely useful linear dimensionality reduction technique.\n",
    "\n",
    "---\n",
    "<a id = \"pca\"></a>\n",
    "\n",
    "### <span style=\"color:#0b486b\">2.1 Principal Component Analysis</span>\n",
    "\n",
    "We'll start with our standard set of initial imports:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function, division\n",
    "\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "\n",
    "# use seaborn plotting style defaults\n",
    "import seaborn as sns; sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Principal Component Analysis is a very powerful unsupervised method for *dimensionality reduction* in data.  It's easiest to visualize by looking at a two-dimensional dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(1)\n",
    "X = np.dot(np.random.random(size=(2, 2)), np.random.normal(size=(2, 200))).T\n",
    "#print(X)\n",
    "plt.plot(X[:, 0], X[:, 1], 'o')\n",
    "plt.axis('equal');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that there is a definite trend in the data. What PCA seeks to do is to find the **Principal Axes** in the data, and explain how important those axes are in describing the data distribution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.7625315 0.0184779]\n",
      "[[-0.94446029 -0.32862557]\n",
      " [-0.32862557  0.94446029]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=2)\n",
    "pca.fit(X)\n",
    "print(pca.explained_variance_)\n",
    "print(pca.components_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see what these numbers mean, let's view them as vectors plotted on top of the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DDw610dAc4Xjz6OrK9/88gK5I0g7WFliWSTiWoqk1Rlfks3pWgwXnbI68rqssmmXfDKKJDPOrAgMna0eRFpk9vjuaIpkyaG6Pcbo9jsrM2xnqbCUBXxSMhpbwmOquuFwurrvus0WDW7ZMzLBOeZGb5o4Yh5u6SWdM3E67Bx3qSo5pWKd3AD7VHsOhq6CA1+0g4HWCQm74BAYPzvMqA8yt8PLBoTa272+lK5rimuWzBgyprFoSpK0zwe4jId7d3cTuIyHaOhODBu3s8ftPdKAoUBJwYVoWXbEUmYw5o3aGOlvJkI4oCL0nDMdST722dh3//d8vAPDqq1v4X//r/0TTtNxn7zrQSjRl4HNquYCXb4K098SpriocPxXB4VBxaCppwwRgToVvTMM6vevLxxMZNE0hY1jMr7I/59DJTsIxe2Vr/92cetux7zRvftCE16VTHnARTxm8+UETFcUeLltW3edYi54keUUBy/rscR7zKgOU+J10x1I9WTgOzp1TjK6rfTJ/JsNET46fqSTgi4Iw3vHy2QsvwBcoIRrupK2tlS2vv836tdf1uZFUlPk4erKDnz5/mrRhUeJ3UlPpz91clp9TzoeHQ7mbTiyZIWUYuJwq8VQGT8/mIgGvY0zlAXrnpacMk0TcwO1QaQpFmV3uY27QT0ckSXs4mTdnPRsU3/qgEQvwuRUUVcHr1klnDP7zzcN8fKQ9FzB3HWglWOJhQXURPp+LaDRJNJEe8jvNmBYXLiwbUHhxLNc70iA+3pv92UQCvigIo51g7K2hJcxru5o476Ir+WDbywD8+3PPc8mKy/rcSLrCSZraYvZmHT2DpYebulk8pxivS+eN9xuZV+nvc9Mp6UlHvGhRee580UR6yEVLQwW67H+Pne6mpSOOqio0t8c40Rym2Ovi9qsXDuilZz8zGxQzhoWuQXs4mfvOovE0hkWfgBlPGswL9i1ZPNx32ntbw6yxlEMYTRCf6MnxM5mM4YuCMJ66K9mAsWL1dbnnjux9jz990tRnovRkSwSHrqIoYJr2nqwO3e5he106nZFUn2JfXZEkhmHSFIqy+0iIrkgy7+Rl77mHX9Xv57dvHx4yvTLb855T4SMcTWOYFm6HjqbBh4dDeecHegdFt0vDQkFTFcLxNOF4GgsFj0vvUysn1tPW0XynYyneNtSfyUjq9wxX/K2QSA9fFITs+HYklqY9nCAST6OpKhuunD/se7O/DrwLl1FcVklXewuJeIQPd73HpauvyvVYo4k0uqbYOzX1FBronb9e4ncSS2bIZEyOnu6mrTOBpqmU+JwA7D3ewQULynJFxDZtPcLx5m5Oh+JomoKqQDieQVftvWQbWiLEkxl0TeXN9xu5q3Zpn/Y2tESoKHHj0O1NxxMpMxcU+/dse/8CWljlZ9+JTrBUMoaBaU8tsLDqsxot2Tz9bMD3eJ254J1vXiBrosohjOYX20T9qjgbSMAXBWFeZYDl55Tz0rbjGKZFwOOkJODkw8Mhqst9w1aLzAaMC1dezR9f/y8Aju3bxjfuuj03UZpNsXTq9urUdMbsk7/+hZVz+OOe07R0xEkkDTRV6SkOqDC/KpArWwDkhis6wym6oyksy8LZU7LAsiw+OZpmVoUPj1MnlTH46HCIf9uyj4xp0doZJ5MxiSczeJz2P/GMYeFxabmg2H9YSFcVYskMhmERTmRwO1WiCXsfWY9bZ1aZhzm9vqNYMsP8qqLcWH5bZxyf2zGi4D1cOYSRjM2PJoj3nszuXWRtqBvT2Ur7h3/4h3+Y7kYMJh5PMXNreQ7O53MR61U5sJDM5Gvfsb+FIp+TBdVFVJR48HvsnnVbV4Kl8wcviubz6Ow70QFAcUkJu/64BYCu9mb+j2/cw+xgEW1dCVIZO9+9pipAVamH7liaaCLDuXNLuOHSeSxbUM7Bhk6iiQzdsTROh0ppwJUL5LPKvLSHkxw/HaaxLUprZ4LTHTEULAwTDMuuKZMxLAzLIuB1oOsq8ZRBLJEhY9oZOYZpcqSpG1W1f2WYFqQzJjWVfgzLAgsOnOwEoMhr/+o42RrhSFM3x06HSaQyeJ06bpdOTZWfutU1dETtP1NHz80rGzDnVQZYOr+UL6xeQE2Fj2KfK883OHLZsfnebdt3ooPKEk+fz+79Z9K/Tf3bUOxzUVnioa0rQXs4SbHPNaFF1mbS33lFUfB6nYO+Lj18UTDGOnHbexgiHphNRfU82k43kEwmePvtN6mtXce8ygDBYID3P2nK9U4vWlQ+oHeaNkzmBX10RVOk0gbheBq/WydjmPZiJ1WxK1S6HbidKpZpkTItNBWwFNwujVTaBAsiMXtYKhxLU+R1kDHsbUary+yJ1Ob2ONFEBr/HwaJZAXTdDowuXRuwIjccS5POmKgqxBIZovEMLqdKOmPwxvuNfGHlHBrbYpNelXKkE6yjHRqSIms2CfiiYIxnLLd3wDCbbuPJJ/8VsEst1Nauy3tcfw0tYU62RGjpsMfkDcMkblqEoyl0XcWyOqku8+D32O1TFAWPSyMcz5AxwOW0e+rZH72dkRQuh4bXpeF26jgcn+VgVJV6cegaN142LxcUS3qGXF7d0UDA+9mN71S7XcCsI5zE6dBwOxXiyYx9A1BUQl0JPjwcmpI0xtHclCWIj55k6YiCMVEZIr0D/DvvbKW7u2vY92SHKmLJNOmMQSKVIZU2SaYMDNPCoSnMqfBxvDlCWcBJOmOSzpiUBtwogAVYJsSTBooCmgKaqtAZTWGYFvGUPSSU1bsuza1XLeJr687n1qsWMa8yMCBjKZ40sLDIpsanDQtNUwEFFPvcw+1gNVFkF6vJJQFfnJHGUiZhLNv75VNTM5/zz78QgEwmzRtvvDZsW5957SCHGrro6E7h0FV0TcUELAuqSj0U+11UlXnxexy0h1MsnlOMaVp0hJPomoJDUzBMu9iY363jdmp4XBoKdq+/stRDPJVh//EOduxv4cCJTuZUePO2p/+NT9cUEikDn9uBZVkYPWk59uJZC4emTlka40TdlEV+MqQjzjjjWTk5UcMAdXXr2Lt3D2AP69x22x1DtjUcS2EY9oKsbMaMaYJpmcRTBpU9qZnzgj72negknsyQMUyKfE6KvE6K/Q72nejCras4HBp+j47bqWOZFl2xNJ+/sLonA8nE73FQFnDnzUDKZsDEEmkaWyMke+YDANxODZdTo7UzTiptomkKzp7nx7o4arTlDGQXq8klAV+ccaZj5WT/4HXRqjUoyg+wLIvt2//Ev/7HH1l23gKWzi3q04ZsWwNeJ+FYGrdTJ57MEO8ZyrFMiMTTLJ5TBIDDoXH+/FJOtccwLQh4dGaVeSn2u2hsi2GYFhXFnwXeeMqgxO+ksS3GkpqSPvMT/csc9L5RFnudNLfHUYAlNSXEUxmOnQqTSBnoqorqMHHqGqYF4XiK1s44a64eeRrjTLgpi4Ek4IszznjKJIxFvuC140iG85ZdwoG9H2JZJk2HtjNv7mx++/ZhygJu0oZJeZGbEy0R5gV9zC73cSoUwwIcukI8ZfesVRWcusqpUIxkyqAjkqK82EU8aXDe3OLcTlAA58wKsOdYB7FEBo9TI56y5wI2XDmfj4+0D/ud9L5R7m+J4OlZfXqqPcbSmlICXieHTnaiqfZisVTGxKEpeN32L4bRBGEpZzAzScAXZ5zJWDk51PDDYMErMHcV7P0QgD273mblVTfT0hEnHEtz4cIyook0bZ1xXLpKVZmXJfOKOdDQRSpj4tRVyotcPcM7OtFEmiOnwnhcGkcak8STBqfaIhT5nFSUeJhd7qOkyM2FC8sIx9N0RlKU+J1suHI+ly2rprEtNux30vtGGU8a9vaF2Bk5YN8gEimT1csqBhQ3aw9/Vkd/JKb6pixGRgK+OONM9MrJ4YYfBgteRXOWo6oapmlw8th+9h04jMdVSsYwc/VdZld4OXY6TENrhHDULgtsWQpel4rf62R2uY8in5OPPm2jO5aCpD1ZaloWpgVd0RQup8bHXXEcqsasoI+lNaUDxsPnVHgHjOGrmtLnO+l9o/S4NNIZE7ByPf3e5R/GezOVcgYz07iydF566SVuuukmbrzxRp555pkBr+/bt4/bb7+dtWvX8uCDD5LJZPJ8ihCjM95sm94ZPv+2ZR8/27SH3YdD7NjXwseHQ2QyZp80xMFSBSsryliwZEXuuYMfb8XCXg1r72DVyulQjHAsRVckRcYwMS0LeuraBDwOinoma0PdCbDAoask0wY9pXgwTOjoTpJMm2i6wrygb0DBtIaWMB8eDjG7wkvA4yQSz9DYFmX5OeUDNizJZsBUl3rsuYSeFb7ZbJgvrJwzIVkykm0zM425h9/c3MxPfvITnn/+eZxOJ3feeSerV69m8eLFuWM2btzI97//fZYvX84DDzzAc889x5e+9KUJabg4O400s2OsE3u9e/OaAruPhugM21UsFdUus5BIGiypKSaUsvdaHewXxRdWzqHt6Oc5sm8nACf2baP4nC8ACm6nRrHPSXs4SdowKfY6SGbswmoZzSCZNtjf0InPreNwaBimZdfWMUzSmb71RFIZk+oiF5qq5H45wGfj4b2HnLKrbKOJNI1tMS7r953lMmBSBkvmldg1fwyT4l51cKrLfePOkpFsm5lpzAF/27ZtXHHFFZSUlACwdu1a6uvr+Zu/+RsAGhsbSSQSLF++HIDbb7+df/3Xf5WALwY1FRtV9J+4NE3QNTvoOZ0OFMUikTFoaI3matQPFrzcSpw5xRkURcGyLMLtjXQ3H6R89jIsy6KtK0E6Y2AaFqFuu9ZKdmhcAUzD5OjpMCvODXJ+TQn7T3YN+CWRXXTVFU0xJ9i3WmV2PHyiV6dOVJaMZNvMPGMO+C0tLQSDn/08q6ys5OOPPx709WAwSHNz86jOUV7uH/6gGSoYLNy/6GO99tfeb6SixIu/p/iT3w+RWIr9J7tZecH4Khseberi3d2n2HGgxc44qdZJG3ZP2uPWiSXsQKvrKsmetMnrr1iQu5ZI2sTn6ybU2cWeXe/w0r/9kfd3bcfqV93PiJzC7bqQdMZC00ySaQtFBcv+sdCnGKCqKswOBrjn9ks42tTF//7tR+w73pF7XQF03V7qmkwbLJpbgq+nMFgklqJmVjHBYICaWcVEYil8vYpm9X59qsjf+ZlvzAHfNM0BM/m9Hw/3+kiEQpGe8rFnlmAwQGvr6DehPhuM59pPnOqye/bRXhkhlsWJU115P3Mkwz8NLWHefL+RT4614/c4cKgKXZEkXZ8m0bLVJE1yG2QkenrYLl3lP1/dT3mRmwq/wosvv8LBj7fScOhDTNPI2/6KWfO54NLrsVQHpmnR3GHn0mfryfdnZEw6uuO0tobxO1SuvmQWh052ks5YqD2lEzRVIZMxMS04cKydmkp/rgjapedV0NoaZuncIrtsQzzVZ8gp+/pUkL/zM+PaVVUZsqM85oBfXV3Nzp07c49bW1uprKzs83pr62e1N9ra2vq8LkR/vTM7uiJJTrXHCMfSBLwOGlrCA1aMDjf8kz3mdCiGz23/VU+mTFIZuxiZYZo92TAWFcUenLpKOGaXIq4o0mk99gGv7XiLQ3v+hJHJV/5WYfaC81m5+looPZ+M4iGWhs5IDMMwyRh2lct88V5TIGOCx/XZRugfHg5RWeKhM5xE1VRMw8KwTBy6RrHPHrfvvUlK720NZbxcjMSYA/6VV17J448/Tnt7Ox6Ph1dffZV/+qd/yr0+Z84cXC4Xu3btYtWqVbz44ousWbNmQhotzk7ZydFwLEVTa4y0YRBNZEinMzz10l4+d0ElybS94XVrZ5wSv3PIhT3Z8fqMYdobgSig6QqpuIWuq2RMe4/WRNJe9ZrJmMRaD9F8eDtbD/yJdDKWt51FwYWU1qyieO5Kzlkwl0sWV9DUFuGTox3omopDAyM7hEO2Jk3fz7AAp0NlflVRn7YunlPM/hOdJNIG8UQaVVUo9btYUlNCsd+VuyH2D+YyXi5GYswBv6qqivvuu4+77rqLdDrNHXfcwcUXX8w999zDt7/9bS666CIeffRRvvvd7xKJRLjgggu46667JrLt4iyT7ak+89pBEukMybRdS8bvdtAVTfK7d49z0aJyqkq9HGnqIha3d3TKrkw7b/MAAB4TSURBVEbtP1EZ6k6gqwrRRCZX+jedMVBUKCty4dBVlswr4dODn/Dp7nc4/Mm7tLXlrwjpLqqmfMGlVC5aTUl5NfGkAdjFzQC6Y2kqSt10hVNkTIWA14mmQkc4ha6Smy9QFNB7droKeBy5NMXsxKviVlhaU8Kp9hgn03aZg7mVPk61xzhyqhu3U8PnGXyDCyGGMq6FVxs2bGDDhg19nnvqqady/7906VJ+85vfjOcUosDMqwwQLPFgWRYZw94IHOzURAWVzkiK6jIfAa+TWCLDqfZYLuD3X9jj0FR2H2kjkTSIpwyi8QwW4HGqhJpPEDv1AfX/7zt0hk7nbYu3qII5567GXb0c3T8bl8PeujA7wZttG0A8mcHt0vGU2/+k0hm7smUsaefUGwn73FpPBUq3U+PPr16U65X3Hs4q9rso9rtIZ0xSaYPmjjgOXcXjtMfn48n4gCEuIUZCVtqKGae8yM2BEx2Yph30dV0lkTTwuLSenjXMLvdx6GQn4Zi9sCffattwNEl3NG2vbgWSkTY6ju+k/cROEl1Nec/t9hUza9FlzD//c1TOPY/WriSZjEEyZWCvmbLrxmuqgmlZ+L2O3L61XZGUvdF4LE0yZeBwqD1j7wouh0rA4ySVMdFUhQ1Xzqe63MemrUcIdSdwaCrt4QSUfJbrX+R1cioUxenQcqmjAMU+B8+8dpBgiWfAZPVYKlSKwiEBX8w4cyq8xBJ2frtTV+3NQAwTD2pukrPI52Ru0E9HJEl7ONlnojIb9D453kEs0klHwy46ju8kGjqW93y608Pqz13DX955B03JSlq7EnRGUvbGIKZFsdeJo1jLFRQzTXuid1a5lzkV/p7zuwh1xUlnLBy6gtOhkkybROJplswtIeBz5QqqZYdxek86x5IZFOyMnPZUz/VcPZvn/3CYeML+heJxaZT5nTR3JDAskyXzSvpMVvf/zMlYxyDObBLwxYzT2BZj4awAJ5qjpA0TtWfWsyOcJp6KkEwZVJV50DSVL99w3oDsnWfrP2bPB1s5+sm7hFsPDpwxBVTNSfWi5Sy64PMsu/Ay5lSV8LnPLcpl9syr9ON16ew52k48mWFBtX2O3plDvc+9aesRWjqTdEeTmKaF26VREtBwOTTm9Ow61dumrUcGFmQrsf/b+9j5VUV9atLsP9EBCgQ8zgGrbgGpUCmGJAFfzCgNLWE+ONQKlj2xmkwbdHQnURQFhw6qAidbI5zuiHHh/NLc++LxGG+//Sb/97/9B0cPvI+VL1deUSmqXkb5gktZeflVXLTYHv6xLCs32ds/xXF+VYD2cAJdt3d90nWV1s44ZQE3r+5oyPXYQ90JVBUqSzy5OjiWZZFIGnlXvI50dWz/sg7hWBpVsYe08r1PKlSKoUjAFxNuqHHkfK+B3Qs93txNqCuJaVk4dXuCNJrI4OiZLFUVMC3wulVUBdq6YvzsV78l0vgB29/9A4lEPE9rFPzBxZTOv5TSeSvQXX6cusL8WRW5I/pP9vZPcezdZl1VUFDQNIWA97NhE5eu4dTtLKDsZG52/iFfhciRVpPsfwMKeB2U+l25omv93ycVKsVQJOCLERvJhODRpq4+48jN7TGeemkv5cUuSnwuOiMpKkrcuTHm598+goVFsMRDPGH3yg3DImbYJXpN0y4mpqlgWGAaBtHWT2k7tp32Ex+STkbzttVbVkPZ/MsombcKp7ck97yigK6pHDzZiaqAQ9co8jqH3M2p9w1g09YjxFMZGloixHsmkkv8TixNpdjvoqG5G8vS7DrzqQyVpZ4+FSKz32H25janwkdlqWfIEs+9z58dcoom0nlLQ09k2Whx9pGAL0ZkpIXN3t19KjeO3B1N0dhmB+R4wqA7GiGezFAacOXGn4/EugFYUF2Um5h06PaOS+3dCWJJA9O0iLcfo+PETtpPvE863pW3jYsWnUPVosuJupfiDNhBtndlDgWoLvWQzBjEEhkCHrsnbDHy8h3Hm7sJdSZxOFTcTntCuak1RnmJi6/dcjHPv3GQw03dgD2pet2quXm3GKypDOByaDS2RUlmTGoq/SNaHTvcqlpZcSuGIgFfjMhIt6xr6Yjl6tI0haKYlmUH73ASXVXwuXWaQtHckEQ6Y6D0DHpnN+UwDJOOSJJk12laj/yJ5iM7SEXb8rarqCTIRZdew7fv+TLnnbeEk60RHnvuQzoj6T5hXFXszbjtQK2TMU1WnGvfFPrv/TqUeE9efXbYxqErpDMm8aTBwtnF3FW7dMTfodflwKGrhLri1FSOvFDgUKtqZcWtGIoE/LPMZOVhj3SS0aGr7DnaTsYwae9O2vu2agpuh4ph2StSzV5ZMw5dy/3/rDIvH+87zJFP/kjz4e3EOhvztkV3+alceClzzruCyy5dxYpzg+xrifHOp/vQVYWSgIvOSLrPe0wL3C6NZMrE41JyuzwNdh2D8bodRONp0hkTh6aSNkwsy8Lba9x8ML2/w+5oik8bu9A1uxqmpFCKqSAB/ywymfXkRzLJ2NASpq0zTjxplzxIZwwMw15V6g+4UVBo6YiTTJu5xVJFXifRSAd/eP1tDn70DieP7c97fs3hpnTeCioXXU7V/PNJJE0sFE63x3lp23FmV3hxOzQONNiLscp7MnyiiQwKdt0aw4BkKk3GMLho0eCTtkOpqfTj0lU6Ikn7Ol06lSUeqsq8o/oOm0LR3K8Ej1uTFEoxJSTgn0VGOuwymKF+HYxkH9ldB1qZVe7DyhgcbY5gmHalSNOycDt00oaJ36ujqSpNLSGaj+zi+L5t7P5oF2aeGsKq5mDO4pXMXrwaM7AYVXMQ8Dp7xs5TBLwOuqIpO4C2xVBV8Lh0InH7V4TDoeHrye7RVIVE2qTE58AwQdOUQVfoDiX7PWTz9LPvH8nWfb2/w1gijUPTSBtmbjhHUijFZJOAfxYZzc5H/Q3362AkJXhD3Ql8PifNnQmKfU5779akQcow6Yql8DlMjLbdHDvwHq/t30U6nR7QDl3XWb5yNd7qFbgrz8fv96Og0BSKoqoKDr0ncAdc+N0OTrZFKfIqdEZTRONpPE57HiCRMlAVBZdDwcLOoKnoKZZ2oiWMz+0Y08TmeEoR936voqigwDmziwatBSTERJOAfxYZaW53PiP5dTDchGB5kZv9Jzpx6CoOXcXvdRBPpog17+X0++9z6vD7pFMDbz6KorBy5Spqa9dz/fVrKS0tzW1cks14qSrzUF3mpbrMxweH2uxevmHi0BTauhI9Y+EW8ZSd1aMqgALxpIXbZU+szq8KEEtmmF9VNGDl62iMZ2I0+95sb1/X1TH90hBiLCTgn0VGMuwymPH8Ouh9/u37W3Bp0Ny4j6N7t9F4aMegufLnn38BtbXruGjVGo63q4S6E7y9p4NVS+y/lkU+JzVVfsqL3Myp8PLh4RDRhN2Lz+796nXpPRPB9m4/pmXZPXtn38qW58wuQtOUGRNUZdMSMR0k4J9FRhNE+o/XOzR727yxrtK0LIvO5qO07nmB3TvfJhHtzHvcwoWLqK1dR23tTcyfvzDvUFLvxVjZ5z48HGL5OeU0tsWwsOiKpnA5VJIpA49LJZm2UFUVl66gopA2LeZU+Ah4dFo6k2RMi2K3Y0YFVUmhFFNNAv5ZZiRBJF+QbQ8n7Hz4ktH9Ovj000PU12/mlVdepqHhRN5jPIFyzrnwSj6/5kbWXbeamp5dniD/UFLvxVjZ58AuqrZqSZDWrjilARed4RRNoSjReIYiv5N4AhRVwe/W0TU7A6YpFCPgdXLjZfMkuIqCJwG/AOUdry+BTMYccjIz+6vg6PETnDzwHof3vMOxo5/mPYfXX0zN0tWUz7+MCy+8mOpyP7Fkhld2NPRJE803lJTOGKQzFvtPdOTKF1SXegiljD5try7zUeRzsPtoO7G4QYnfQag7SWsijc+t4zOdaIpKid8pOe5CIAF/Wkz3JhWDjde3p5KDTmZ+fOAYT/3qvzi85x2ajh/Ie4zf76euro5rr72Ryy//HL9790SfSeR8E8H5JppNCyLxNF63nitfcPBkF3Mq7AlbyzLxuh3MLvfRHUvbv1LiGVBUKkrctHcnSRsWXrfOrDJvbi9YyXEXhU4C/hSbzMVRIzXSbJ7u7m5+//vX2LJlM9u3v4dlDcyV1x1Orrn6Wurq1vFnf3Y1c+dW0NoaBkY2EZxvotkwLFxOe0jGLrtgbzDe0BLB53GAZadeftrYRdow8Tp1Al4nS2vscsnvH2xBQck9znfe6b7pCjEdJOBPsfEujpoIQ2XzxONx/vCHN9myZTPv/PEPZPLkyiuqyjlLV3LhyqupWriCb/75pXnPM5IbS76J5upyDyU+F6faY7nVrF63TtqwqKn0c7ipG9Oy6IokiScNVFUhWOymK5Kk2O/qU64h33lnwk1XiOkgAX+KTUT643j1D7LFXg1P4gRP/uQXvPXW74nHY3nfVzZ7CSsuv5aVq6/G5y8eEMz7G2maaP+J5k1bjxBNpPv00Hfsbybg6Sm4Zhi0tiewFLsCpqZCZyTFvuMdLJgVoMjrxMIatITwTLjpCjEdJOBPsfEsjppIs8u9nHa3svutzfzq9Vfo6spfcnjWvMVctOoaapaupqlbt3vbPnvbveGyeOZVBlh+TjlvvN9IZyRFid/JF1bOGTao5rtRaKqKQ1c43NRNMmXicKiYpoWiKJQVuYgn7ZLHnZEUX77hPIBB01Nnwk1XiOkw5oDf1NTExo0bCYVCLFy4kEcffRSfz9fnmMbGRtavX09Njb3BckVFBU8//fT4WnyGG8/iqPGyLItPPtlDff3veOWVLbS2tuQ9bsGChVQuupzLr/wCFVVzc8/7ixIcPR0esGn4YBpawnx4OMS8Sj9L5tnX+uHhENXlviHfl2+YZ8OV83lp23GA3CbigJ2LnzapLPEQS6YJlnj6rAzOZ6bcdIWYamMO+P/4j//Il770JdatW8dPf/pTnnzySTZu3NjnmD179rBhwwYeeuihcTf0bDEdKywPH/6U+vrN1Ne/TEPD8bzHVFfPYu3am6irW8eSJct48Z2jRBN9x+8dDo0V5wZHXJZgPEMn+dYTvLu3hWg8RXcUVEXB6VBRVYVILE0qbaBpKslUhk1bjww5GTudN10hptOYAn46nWbHjh389Kc/BeD222/nL//yLwcE/N27d3Pw4EFuueUWiouLefDBB1myZMn4W32Gm4oVlo2NJ3nllZepr9/MwYP50yhLS8u44YZa6urWccklK1BVNffaRATFiR46qan0E02kmV3uY9/xDsLxNKlkBlVRsExAhUMnO1FVhapS76CTsVLWQBSqMQX8jo4O/H4/um6/PRgM0tzcPOA4l8vFzTffzJ133snWrVu59957efnll3E6nQOOnSnO5HS9UKiN116rZ8uWzXz00Qd5j/H5fFx33Q3U1a3j8ss/l/sz7G8sQbGhJcxr7zdy4lTXhJRr6C97E/K6dJbWlPD+oTYUFJwOjYpiNxYWpmlP4FaX+Yb8RSFlDUQhGjbgb9myhUceeaTPc/Pnz0dRlD7P9X8M8K1vfSv3/1dffTU/+tGPOHLkCEuXDr4NXG/l5SPf9m0iHG3q4q2PTuH3OJhbXUQsnuatj05x2zU+Fs4uHtVnBYNTE0y6urqor69n06ZNvPPOO3nryrtcLq6//npuvfVWrrvuOtzukQXcYDDAygtG1qPP991FkhkUFLweJ16Pg1g8jYnC9VcsGNP3EwwGKC318e7uU7R0xAiWejm3poTyIg8A7+05hd+rEU8a+Hz2LwuP10lbZ3zK/jym6jwzkVz7zDdswK+rq6Ourq7Pc+l0mtWrV2MYBpqm0draSmVl5YD3/vrXv2b9+vWUltrpdZZlDdqjzCcUimCaI99gerxef+8YKhaKZRGPpVAAFYvX3zs2qnK6wWAgt/hoMti58m9RX7+Zd955O29deU3TuOKKK6mtXce1116P32/fPMPhNOHwwONHYqhfP9nvzu91Eo0mUYBir4NMxkSxLE6e7qa8yM01l8zC71DH/P34HSo3rJwDfJa+GY0mAXBoCt2RFF63nnsuOzk7mX8eWZP95z6TybXPjGtXVWXIjvKYhnQcDgeXXnopL7/8Mhs2bGDTpk2sWbNmwHE7duwgkUhwzz33sH37dkzTZNGisdchn2yTma433qGidDrNu+/+kfr6zbz11hvEYvlz5VeuvJTa2nVcf/1aysrKJqxNwy1WGku5hvHqP89Q6nfR3p1gdoVXaswLkYdiWdaYutCNjY3cf//9hEIhZs2axY9//GOKi4t59tlnaWlp4Tvf+Q7Nzc3cf//9tLa24nK5ePjhh0c8nANT38PP9hh7jzlnH4+nh987WPaeAB1uZadpmrz//k7q6zfz2mv1g+bKL1t2PrW161i79iaqq2eNqI2jbdNw30329cpy/4De9WQF/Ox19L5pzanw0tgWm5Y5mJnU05tqcu0z49qH6+GPOeBPhakO+GMNzP31/wswmhuJZVns3bunp+TwFlpaBk6Gg50rn60rv2DB6ANq/zZ1R1Mcbw6TMUxWnBscECif3ryXsoCrz1yNZVm0h5N8bd35ue+uosQLpjnm7+5MNpP+4U81ufaZce2TMqRztpqsdL2RDBUdOXKY+vrNbNmyedBc+aqqatauvYmbblrPkiXL8k6Uj6VN3dEUnzZ29WwTSN50xuEWK2W/u/0nu3NZOpLqKMTMIgG/n8lI1xssWGqZLn7xi6fYsmUzBw/uz/ve0tJSrr/ezpVfvnxln1z53kY7R9C7TU2hKA7d/lyPW8ubzjiSvPx5lXZWz0zp7Qgh+pKAPwV6B0srFeGDHW/zyftvc/pE/gVRPp+Pa6+9nrq69Vx++RU4HIMXKIOxVX/s3aZYIo1D00gbJjWV9s/B/r9AZLGSEGc+CfhToMQDeudHPP+733Hk4IdYeXLlnU4na9ZcQ22tXVd+pLnyMLYSBr0DuKKooNgbfRf77WGefAukZLGSEGc2CfiTJJFI9MmVT6VSA44ZLFd+tMaaTpoN4Nnevq6rks4oxFlMAv4ESqfTvPfeNt588xXq6+sHzZVfsWIVtbXruOGG2mFz5UdivNUfZbhGiMIgAX+ceufKv/76K3R2duY9bunSbK58HbNmTWzPeSIKnclwjRBnPwn4Y2BZFvv2fZLLlW9uPp33uPnzF1Bbu466unVjypUfKemhCyFGQgL+KBw9eoQtW35Hff1mTpwYPFf+tttu5eqrb2Dp0vPHlSs/GtkeejY989UdDWdctU8hxOSSgD+MU6eaqK+368ofOLAv7zElJSXccEMttbXrWLFiFVVVxdOSiy6bcwshhiIBP4/29hCvvlpPff1mPvzw/bzHeL1errvuBmpr17F69eeGzZWfCrI5txBiKBLwe4TDYX7/+9eor9/M9u3vYRjGgGOcTidXXXU1tbXruOqqa0aVKz8VZHNuIcRQCjrgJxIJtm61c+W3bh08V3716s/lcuUDgZnbU5bNuYUQQym4gJ9Op/nTn96lvn4zb775OtFoNO9xn+XKr6WsrHzM55vKLRNlc24hxFDOuoCfL8DOqfDxwQe7crnyHR0ded+7ZMmyXMnhiciVn+pJVEnPFEIM5awK+L0DbKnfyZFP97PpP/43Jw+8R1tbS9731NTM78mVX8/ChRObKz8dk6iygEoIMZizKuBnA2yo6RAvPvMYoZaTeY+rrKyitvYmamvXsWzZBZOWKy+TqEKImeSsCvjZAPvr/3pyQLDvnys/WF35iSSTqEKImeSsCvjZADtv4fmcPnkEp8vD4gtWs+Lya/mfX7t9ynPlZRJVCDGTnFUBPxtg16z/Kn92w//A0j2kTY3ay2umZWGUTKIKIWaSsyrg9wmwGSgPTH8tGZlEFULMFGdVwAcJsEIIMZjJn7kUQggxI4w74D/22GM8/vjjeV9LpVJs3LiRuro6brvtNg4fPjze0wkhhBijMQf8cDjMAw88wC9+8YtBj/n1r3+Nx+Nhy5YtPPDAA/zd3/3dWE8nhBBinMYc8N944w0WLFjA3XffPegxb731FjfffDMAl112Ge3t7TQ1NY31lEIIIcZhzAH/1ltv5etf/zqapg16TEtLC8FgMPc4GAxy+nT+7QCFEEJMrmGzdLZs2cIjjzzS57lFixbxy1/+ctgPtyyrT9kCy7JGtcK1vNw/4mNnmmCwcDOF5NoLk1z7zDdswK+rq6Ourm5MH15VVUVLSws1NTUAtLW1UVlZOeL3h0IRTNMa07mnUzAYmJYtDmcCuXa59kIzk65dVZUhO8qTmpZ59dVX8+KLLwKwc+dOXC4Xs2dLWQEhhJgOEx7wn332Wf7lX/4FgL/6q78ilUqxbt06Hn74YX7wgx9M9OmEEEKMkGJZ1owdM5EhnTOPXLtce6GZSdc+rUM6QgghZg4J+EIIUSAk4AshRIGQgC+EEAVCAr4QQhQICfhCCFEgJOALIUSBkIAvhBAFQgK+EEIUCAn4QghRICTgCyFEgZCAL4QQBUICvhBCFAgJ+EIIUSAk4AshRIGQgC+EEAVCAr4QQhQICfhCCFEgJOALIUSBkIAvhBAFQgK+EEIUCAn4QghRIPTxfsBjjz2Gpml861vfGvBaY2Mj69evp6amBoCKigqefvrp8Z5SCCHEGIw54IfDYR555BE2b97MX//1X+c9Zs+ePWzYsIGHHnpozA0UQggxMcY8pPPGG2+wYMEC7r777kGP2b17NwcPHuSWW27hrrvu4sCBA2M9nRBCiHEac8C/9dZb+frXv46maYMe43K5uPnmm3nhhRf42te+xr333ksqlRrrKYUQQoyDYlmWNdQBW7Zs4ZFHHunz3KJFi/jlL38JwOOPPw6Qdwy/v5tvvpkf/OAHLF26dIzNFUIIMVbDjuHX1dVRV1c3pg//9a9/zfr16yktLQXAsix0feTTBqFQBNMc8n40IwWDAVpbw9PdjGkh1y7XXmhm0rWrqkJ5uX/w1yfz5Dt27OA3v/kNANu3b8c0TRYtWjSZpxRCCDGIcadl9vfss8/S0tLCd77zHR588EHuv/9+XnzxRVwuFz/60Y9QVUn9F0KI6TDsGP50kiGdM49cu1x7oZlJ1z6tQzpCCCFmDgn4QghRICTgCyFEgZCAL4QQBUICvhBCFAgJ+EIIUSAmPA9/IqmqMt1NGLMzue3jJddemOTap99w7ZjRefhCCCEmjgzpCCFEgZCAL4QQBUICvhBCFAgJ+EIIUSAk4AshRIGQgC+EEAVCAr4QQhQICfhCCFEgJOALIUSBkIA/CXbt2sUdd9zBLbfcwle+8hUaGxunu0lT7rHHHuPxxx+f7mZMiZdeeombbrqJG2+8kWeeeWa6mzPlIpEI69ev5+TJk9PdlCn1xBNPsG7dOtatW8cPfvCD6W7OiEjAnwQbN27k+9//Pi+++CIbNmzg+9///nQ3acqEw2EeeOABfvGLX0x3U6ZEc3MzP/nJT/j3f/93Nm3axH/+53/y6aefTnezpsxHH33EF7/4RY4dOzbdTZlS27Zt45133uGFF15g06ZNfPLJJ7z22mvT3axhScCfYKlUiu985zssXboUgCVLlnDq1KlpbtXUeeONN1iwYAF33333dDdlSmzbto0rrriCkpISvF4va9eupb6+frqbNWWee+45vve971FZWTndTZlSwWCQ+++/H6fTicPh4JxzzqGpqWm6mzWsGV0t80zkdDq55ZZbADBNkyeeeILrr79+mls1dW699VaAghnOaWlpIRgM5h5XVlby8ccfT2OLptbDDz883U2YFueee27u/48dO8aWLVt49tlnp7FFIyMBfxy2bNnCI4880ue5RYsW8ctf/pJUKsX9999PJpPhG9/4xjS1cPIMde2FxDRNFOWzkrSWZfV5LM5uhw4d4hvf+AZ/+7d/y4IFC6a7OcOSgD8OdXV11NXVDXg+Go3yzW9+k5KSEn72s5/hcDimoXWTa7BrLzTV1dXs3Lkz97i1tbXghjcK1a5du/j2t7/NAw88wLp166a7OSMiY/iTYOPGjcyfP5/HHnsMp9M53c0Rk+jKK6/k3Xffpb29nXg8zquvvsqaNWumu1likp06dYp7772XRx999IwJ9iA9/Am3d+9e3njjDRYvXsxtt90G2OO6Tz311DS3TEyGqqoq7rvvPu666y7S6TR33HEHF1988XQ3S0yyp59+mmQyyT//8z/nnrvzzjv54he/OI2tGp7seCWEEAVChnSEEKJASMAXQogCIQFfCCEKhAR8IYQoEBLwhRCiQEjAF0KIAiEBXwghCoQEfCGEKBD/P1yoxKiAz8EEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X[:, 0], X[:, 1], 'o', alpha=0.5)\n",
    "for length, vector in zip(pca.explained_variance_, pca.components_):\n",
    "    v = vector * 3 * np.sqrt(length)\n",
    "    plt.plot([0, v[0]], [0, v[1]], '-k', lw=3)\n",
    "plt.axis('equal');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that one vector is longer than the other. In a sense, this tells us that that direction in the data is somehow more \"important\" than the other direction.\n",
    "The explained variance quantifies this measure of \"importance\" in direction.\n",
    "\n",
    "Another way to think of it is that the second principal component could be **completely ignored** without much loss of information! Let's see what our data look like if we only keep 95% of the variance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(200, 2)\n",
      "(200, 1)\n"
     ]
    }
   ],
   "source": [
    "clf = PCA(0.95) # keep 95% of variance\n",
    "X_trans = clf.fit_transform(X)\n",
    "print(X.shape)\n",
    "print(X_trans.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By specifying that we want to throw away 5% of the variance, the data is now compressed by a factor of 50%! Let's see what the data look like after this compression:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_new = clf.inverse_transform(X_trans)\n",
    "plt.plot(X[:, 0], X[:, 1], 'o', alpha=0.2)\n",
    "plt.plot(X_new[:, 0], X_new[:, 1], 'ob', alpha=0.8)\n",
    "plt.axis('equal');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The light points are the original data, while the dark points are the projected version.  We see that after truncating 5% of the variance of this dataset and then reprojecting it, the \"most important\" features of the data are maintained, and we've compressed the data by 50%!\n",
    "\n",
    "This is the sense in which \"dimensionality reduction\" works: if you can approximate a data set in a lower dimension, you can often have an easier time visualizing it or fitting complicated models to the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Application of PCA to Digits\n",
    "\n",
    "The dimensionality reduction might seem a bit abstract in two dimensions, but the projection and dimensionality reduction can be extremely useful when visualizing high-dimensional data.  Let's take a quick look at the application of PCA to the digits data we looked at before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "digits = load_digits()\n",
    "X = digits.data\n",
    "y = digits.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1797, 64)\n",
      "(1797, 2)\n"
     ]
    }
   ],
   "source": [
    "pca = PCA(2)  # project from 64 to 2 dimensions\n",
    "Xproj = pca.fit_transform(X)\n",
    "print(X.shape)\n",
    "print(Xproj.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Xproj[:, 0], Xproj[:, 1], c=y, edgecolor='none', alpha=0.5,\n",
    "            cmap=plt.cm.get_cmap('nipy_spectral', 10))\n",
    "plt.colorbar();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This gives us an idea of the relationship between the digits. Essentially, we have found the optimal stretch and rotation in 64-dimensional space that allows us to see the layout of the digits, **without reference** to the labels."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What do the Components Mean?\n",
    "\n",
    "PCA is a very useful dimensionality reduction algorithm, because it has a very intuitive interpretation via *eigenvectors*.\n",
    "The input data is represented as a vector: in the case of the digits, our data is\n",
    "\n",
    "$$\n",
    "x = [x_1, x_2, x_3 \\cdots]\n",
    "$$\n",
    "\n",
    "but what this really means is\n",
    "\n",
    "$$\n",
    "image(x) = x_1 \\cdot{\\rm (pixel~1)} + x_2 \\cdot{\\rm (pixel~2)} + x_3 \\cdot{\\rm (pixel~3)} \\cdots\n",
    "$$\n",
    "\n",
    "If we reduce the dimensionality in the pixel space to (say) 6, we recover only a partial image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 950.4x172.8 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#download the fig_code zipped file (from week 6 or 8 folder on clouddeakin) and unzip the folder in the same folder as your .py file\n",
    "from fig_code.figures import plot_image_components\n",
    "\n",
    "sns.set_style('white')\n",
    "plot_image_components(digits.data[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But the pixel-wise representation is not the only choice. We can also use other *basis functions*, and write something like\n",
    "\n",
    "$$\n",
    "image(x) = {\\rm mean} + x_1 \\cdot{\\rm (basis~1)} + x_2 \\cdot{\\rm (basis~2)} + x_3 \\cdot{\\rm (basis~3)} \\cdots\n",
    "$$\n",
    "\n",
    "What PCA does is to choose optimal **basis functions** so that only a few are needed to get a reasonable approximation.\n",
    "The low-dimensional representation of our data is the coefficients of this series, and the approximate reconstruction is the result of the sum:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4b2d8e65607f4a769601025ec0db0e0e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=0, description='i', max=1796), Output()), _dom_classes=('widget-interact…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from fig_code.figures import plot_pca_interactive\n",
    "\n",
    "plot_pca_interactive(digits.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we see that with only six PCA components, we recover a reasonable approximation of the input! (To view the output, load the .ipynb in Jupyter)\n",
    "\n",
    "Thus we see that PCA can be viewed from two angles. It can be viewed as **dimensionality reduction**, or it can be viewed as a form of **lossy data compression** where the loss favors noise. In this way, PCA can be used as a **filtering** process as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choosing the Number of Components\n",
    "\n",
    "But how much information have we thrown away?  We can figure this out by looking at the **explained variance** as a function of the components:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set()\n",
    "pca = PCA().fit(X)\n",
    "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n",
    "plt.xlabel('number of components')\n",
    "plt.ylabel('cumulative explained variance');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we see that our two-dimensional projection loses a lot of information (as measured by the explained variance) and that we'd need about 20 components to retain 90% of the variance.  Looking at this plot for a high-dimensional dataset can help you understand the level of redundancy present in multiple observations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PCA as data compression\n",
    "\n",
    "As we mentioned, PCA can be used for is a sort of data compression. Using a small ``n_components`` allows you to represent a high dimensional point as a sum of just a few principal vectors.\n",
    "\n",
    "Here's what a single digit looks like as you change the number of components:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 64 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(8, 8, figsize=(8, 8))\n",
    "fig.subplots_adjust(hspace=0.1, wspace=0.1)\n",
    "\n",
    "for i, ax in enumerate(axes.flat):\n",
    "    pca = PCA(i + 1).fit(X)\n",
    "    im = pca.inverse_transform(pca.transform(X[20:21]))\n",
    "\n",
    "    ax.imshow(im.reshape((8, 8)), cmap='binary')\n",
    "    ax.text(0.95, 0.05, 'n = {0}'.format(i + 1), ha='right',\n",
    "            transform=ax.transAxes, color='green')\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take another look at this by using IPython's ``interact`` functionality to view the reconstruction of several images at once: (To view the output, load the .ipynb in Jupyter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e99cfba239a24998952aa115de567c2f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='n_components', options=(1, 32, 64), value=1), Output()), _dom_clas…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ipywidgets import interact\n",
    "\n",
    "def plot_digits(n_components):\n",
    "    fig = plt.figure(figsize=(8, 8))\n",
    "    plt.subplot(1, 1, 1, frameon=False, xticks=[], yticks=[])\n",
    "    nside = 10\n",
    "    \n",
    "    pca = PCA(n_components).fit(X)\n",
    "    Xproj = pca.inverse_transform(pca.transform(X[:nside ** 2]))\n",
    "    Xproj = np.reshape(Xproj, (nside, nside, 8, 8))\n",
    "    total_var = pca.explained_variance_ratio_.sum()\n",
    "    \n",
    "    im = np.vstack([np.hstack([Xproj[i, j] for j in range(nside)])\n",
    "                    for i in range(nside)])\n",
    "    plt.imshow(im)\n",
    "    plt.grid(False)\n",
    "    plt.title(\"n = {0}, variance = {1:.2f}\".format(n_components, total_var),\n",
    "                 size=18)\n",
    "    plt.clim(0, 16)\n",
    "    \n",
    "interact(plot_digits, n_components=[1, 32, 64], nside=[1, 8]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id = \"others\"></a>\n",
    "\n",
    "### <span style=\"color:#0b486b\">2.2 Other Dimensionality Reducting Routines</span> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that scikit-learn contains many other unsupervised dimensionality reduction routines: some you might wish to try are\n",
    "Other dimensionality reduction techniques which are useful to know about:\n",
    "\n",
    "- [sklearn.decomposition.PCA](http://scikit-learn.org/0.13/modules/generated/sklearn.decomposition.PCA.html): \n",
    "   Principal Component Analysis\n",
    "- [sklearn.decomposition.RandomizedPCA](http://scikit-learn.org/0.13/modules/generated/sklearn.decomposition.RandomizedPCA.html):\n",
    "   extremely fast approximate PCA implementation based on a randomized algorithm\n",
    "- [sklearn.decomposition.SparsePCA](http://scikit-learn.org/0.13/modules/generated/sklearn.decomposition.SparsePCA.html):\n",
    "   PCA variant including L1 penalty for sparsity\n",
    "- [sklearn.decomposition.FastICA](http://scikit-learn.org/0.13/modules/generated/sklearn.decomposition.FastICA.html):\n",
    "   Independent Component Analysis\n",
    "- [sklearn.decomposition.NMF](http://scikit-learn.org/0.13/modules/generated/sklearn.decomposition.NMF.html):\n",
    "   non-negative matrix factorization\n",
    "- [sklearn.manifold.LocallyLinearEmbedding](http://scikit-learn.org/0.13/modules/generated/sklearn.manifold.LocallyLinearEmbedding.html):\n",
    "   nonlinear manifold learning technique based on local neighborhood geometry\n",
    "- [sklearn.manifold.IsoMap](http://scikit-learn.org/0.13/modules/generated/sklearn.manifold.Isomap.html):\n",
    "   nonlinear manifold learning technique based on a sparse graph algorithm\n",
    "   \n",
    "Each of these has its own strengths & weaknesses, and areas of application. You can read about them on the [scikit-learn website](http://sklearn.org)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <span style=\"color:#0b486b\">Tasks</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try the provided examples and get yourself familiar with sample code before attempting portolio tasks.\n",
    "\n",
    "Please show your attempt to your tutor before you leave the lab, or email your files to your coordinator if you are an off-campus student.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <span style=\"color:#0b486b\">Summary</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this session we have covered: \n",
    " - feature selection and dimensionality reduction methods.\n",
    " - how to apply the methods to select feature and reduce dimensionality in Python."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <span style=\"color:#0b486b\">Acknowledgement</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The PCA part was put together by [Jake Vanderplas](http://www.vanderplas.com) for PyCon 2015. Source and license info is on [GitHub](https://github.com/jakevdp/sklearn_pycon2015/).\n",
    "\n",
    "- Feature Selection and Dimensionality Reduction, by Tara Boyle, on [Kaggle.com](https://www.kaggle.com/tboyle10/feature-selection/data).\n"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
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